# TODO: 导入必要的库和模块
from sklearn.datasets import load_digits
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import matplotlib.pyplot as plt
import pickle
# TODO: 加载数字数据集
digits = load_digits()
X = digits.data
y = digits.target
# TODO: 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# TODO: 初始化变量以存储最佳准确率，相应的k值和最佳knn模型
best_accuracy = 0
best_k = 0
best_knn_model = None
# TODO: 初始化一个列表以存储每个k值的准确率
accuracies = []
# TODO: 尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
for k in tqdm(range(1, 41)):
    knn = KNeighborsClassifier(n_neighbors=k)
    knn.fit(X_train, y_train)
    accuracy = knn.score(X_test, y_test)
    accuracies.append(accuracy)
    if accuracy > best_accuracy:
        best_accuracy = accuracy
        best_k = k
        best_knn_model = knn
# TODO: 将最佳KNN模型保存到二进制文件
with open("best_knn_model.pkl",'wb') as f:
	pickle.dump(best_knn_model,f)
# TODO: 打印最佳准确率和相应的k值
print(f"最佳准确率: {best_accuracy:.2f}，对应的K值: {best_k}")

# 创建准确率折线图
plt.plot( accuracies, linestyle='-')
plt.xlabel('K value')
plt.ylabel('Accuracy')
plt.title('Accuracy of different K values')

# 添加垂直红线和标记
plt.axvline(x=best_k, color='red')
plt.text(best_k + 0.5, best_accuracy, f'K={best_k}, Accuracy={best_accuracy:.2f}', color='red')

# 保存成PDF文件
plt.savefig('accuracy_plot.pdf')

# 显示图形
plt.show()